Data Analytics with R Programming Certification Training

Module 1: R Environment & Data Import

⏳ 2 hours

• Topics: Installing R/RStudio, package management, working directory, data types

• Hands-on: Load CSV, Excel, and JSON datasets; inspect with str(), glimpse(), and summary functions

Module 2: Data Wrangling with dplyr & tidyr

⏳ 3 hours

• Topics: filter(), select(), mutate(), summarize(), group_by(), pivot_longer(), pivot_wider()

• Hands-on: Clean messy survey data, reshape wide ↔ long, derive new variables

Module 3: Exploratory Data Visualization

⏳ 3 hours

• Topics: Grammar of graphics, ggplot2 aesthetics, scales, facets, themes

• Hands-on: Create and customize multi-panel plots to reveal trends and outliers

Module 4: Statistical Analysis in R

⏳ 2.5 hours

• Topics: Descriptive stats, confidence intervals, t-tests, chi-square tests, one-way ANOVA

• Hands-on: Test differences in group means and associations between categorical variables

Module 5: Predictive Modeling with caret

⏳ 4 hours

• Topics: Data partitioning, cross-validation, training linear/logistic regression, decision trees, random forests

• Hands-on: Build and compare model performance (RMSE, accuracy), tune hyperparameters

Module 6: Advanced Visualization & Reporting

⏳ 2 hours

• Topics: Interactive plots with plotly, dashboards with Shiny, reproducible reports with R Markdown

• Hands-on: Deploy a Shiny app showcasing key metrics; generate a PDF report from R Markdown

Module 7: Capstone Project – End-to-End Analytics Workflow

⏳ 4 hours

• Topics: Project scoping, data pipeline, analysis, modeling, visualization, and presentation

• Hands-on: Execute a complete analytics case study (e.g., customer churn, sales forecasting) and deliver an interactive dashboard

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